Artificial Intelligence and Causal Inference address the recent
development of relationships between artificial intelligence (AI)
and causal inference. Despite significant progress in AI, a great
challenge in AI development we are still facing is to understand
mechanism underlying intelligence, including reasoning, planning and
imagination. Understanding, transfer and generalization are major
principles that give rise intelligence. One of a key component for
understanding is causal inference. Causal inference includes
intervention, domain shift learning, temporal structure and
counterfactual thinking as major concepts to understand causation and
reasoning. Unfortunately, these essential components of the causality
are often overlooked by machine learning, which leads to some failure of
the deep learning. AI and causal inference involve (1) using AI
techniques as major tools for causal analysis and (2) applying the
causal concepts and causal analysis methods to solving AI problems. The
purpose of this book is to fill the gap between the AI and modern causal
analysis for further facilitating the AI revolution. This book is ideal
for graduate students and researchers in AI, data science, causal
inference, statistics, genomics, bioinformatics and precision medicine.
Key Features:
- Cover three types of neural networks, formulate deep learning as an
optimal control problem and use Pontryagin's Maximum Principle for
network training.
- Deep learning for nonlinear mediation and instrumental variable causal
analysis.
- Construction of causal networks is formulated as a continuous
optimization problem.
- Transformer and attention are used to encode-decode graphics. RL is
used to infer large causal networks.
- Use VAE, GAN, neural differential equations, recurrent neural network
(RNN) and RL to estimate counterfactual outcomes.
- AI-based methods for estimation of individualized treatment effect in
the presence of network interference.
